A data analysis of the 2024 Nigeria Demographic and Health Survey, examining malaria burden, intervention coverage, and socioeconomic inequities across all 36 states and the FCT.
Nigeria bears one of the heaviest malaria burdens in the world. Understanding how geography, wealth, and intervention coverage interact with disease outcomes is critical for targeted public health policy.
The 2024 Nigeria Demographic and Health Survey (NDHS) is a nationally representative cross-sectional survey conducted December 2023 through May 2024 under DHS Phase 8. Children's Recode file (NGKR8BFL) containing 27,783 child records was used as the primary data source, with malaria RDT biomarker results for 12,795 children under five years of age.
This analysis builds directly on the author's undergraduate thesis; a 12-week field study on mosquito species distribution and breeding site ecology in Kwara State, Nigeria. The present project extends that inquiry from local entomology to national-scale epidemiological data, applying data analytics methods to the disease outcomes linked to the vectors studied at the grassroots level.
Analysis covers malaria RDT test results (ML0), ITN/net usage (ML101), fever incidence (H22), wealth index (V190), residency type (V025), and geopolitical zone and state identifiers. Hemoglobin levels (HW53) were examined as an anemia proxy. Only children with valid RDT results were included in prevalence calculations.
Data cleaning and variable labelling was done in Excel,while transformation and modelling were performed in Power BI Power Query. DAX measures were written for positivity rate calculations, conditional formatting, and KPI cards. The final dashboard was built in Power BI Desktop with custom background design and published for portfolio presentation.
Six findings emerge from this analysis spanning geography, wealth, intervention coverage, and healthcare-seeking behaviour among caregivers of under-five children.
| Zone | Negative | Positive | Total Tested | Positivity Rate | Visual |
|---|---|---|---|---|---|
| North West | 1,773 | 2,113 | 3,913 | 54% | |
| North East | 1,241 | 1,367 | 2,629 | 52% | |
| North Central | 1,485 | 878 | 2,378 | 36.92% | |
| South South | 850 | 425 | 1,287 | 33.02% | |
| South East | 983 | 355 | 1,345 | 26.39% | |
| South West | 933 | 308 | 1,243 | 24.78% |
More than half of surveyed children slept without any mosquito net the night before the survey, representing the single largest intervention gap identified in this analysis.
Less than half of children slept under an insecticide-treated net despite ITN distribution being a cornerstone of Nigeria's National Malaria Elimination Programme (NMEP).
This analysis followed a structured four-stage process from raw DHS data to published dashboard.
Access to Nigeria DHS 2024 (Phase 8) microdata was obtained through the DHS Program portal. Children's Recode file (NGKR8BFL) was selected as the primary dataset containing child-level biomarker, household, and geographic variables.
Cleaning was done in Excel and numeric codes were replaced with descriptive labels across all key variables. Rows without RDT results were excluded from prevalence calculations. State and zone codes were mapped to names.
DAX measures were written for malaria positivity rate, RDT tested count, total positive cases, and conditional KPI formatting. Cross-tabulations were built across zone, state, wealth index, and residency type to surface equity-relevant patterns.
An interactive Power BI dashboard was built featuring a Nigeria state-level choropleth map, donut chart, stacked bar charts, and KPI cards. Zone and state slicers enable dynamic filtering. Dashboard background was custom-designed with public health thematic elements.
These findings point to four evidence-based areas where targeted intervention could meaningfully reduce malaria burden among Nigerian children under five.
With positivity rates exceeding 50% in both zones — and Kebbi, Katsina, and Zamfara as the three hardest-hit states — resources for bed net distribution, indoor residual spraying, and community health worker deployment should be concentrated in these areas first.
55.4% of children sleeping without nets despite national distribution campaigns suggests a behavioural and access gap — not purely a supply problem. Community-level education and follow-up verification of net use should accompany future distribution rounds.
The 17.6 percentage point gap between the Poorer and Richest quintiles confirms that malaria in Nigeria is partly a poverty disease. Conditional health support schemes and free treatment access for children in the two lowest wealth quintiles would reduce this inequity.
Rural children face 6 percentage point higher positivity rate than urban children. Expanding community-based RDT testing, artemisinin-based therapy availability, and rural health post staffing would directly address this disparity.
The full project is available below — read the written report, explore the interactive dashboard, or download the source files.
Written analysis covering methodology, findings, and policy recommendations in full detail.
Download the native Power BI file to explore the data model, DAX measures, and visuals directly.
View the interactive, published version on Power BI Service — filter by zone, state, and demographic.
A static, print-ready PDF snapshot of the full dashboard for offline viewing and quick reference.